Data masking refers to obscuring particular records of sensitive data within the database. Masking of data makes sure that confidential information is substituted with realistic but not the real set of data in the testing environment. It helps in accomplishing both the aims – protecting sensitive data and making sure that test data is valid and testable. Data masking is not merely about Test Data. In fact, this approach applies to every situation where an organization does not want to expose real information.
There are many ways to implement data masking. It could be as a replacement of existing records with particular test data or jumbling of individual characters or numbers, consequently creating a new file. These days almost every organization is facing challenges to protect their databases from security breaches. Here are the four possible steps to implement data masking that can help you making your organization’s database security more static.
This stage includes recognizing and classifying sensitive or synchronized data throughout the organization. It is performed by business or security analysts, the aim of this exercise is to generate a comprehensive record of sensitive data elements specific to the enterprise and find out the related tables, columns, and relationships across organization databases that hold the confidential information.
In this step, developers or database administrators in combination with business or security analysts classify the masking algorithms that symbolize the best possible methods to substitute the original sensitive data. Developers can influence the existing masking library or expand it with their masking practices.
This step and the next one may be insistent. The security administrator carries out the masking procedure to protect the sensitive information throughout the masking trials. Once the masking process has concluded and has been verified, the database administrators then hand over the situation to the application testers.
In the final step, the production users implement application procedures to examine whether the resultant masked data can be shifted over to the other non-production users. If the masking methods need to be tweaked further, the DBA restores the database to the pre-masked state, fixes the masking algorithms and re-executes the masking process.